Anomalous Event Detection in Videos Using Supervised Classifier
Observing and modeling human behavior and activity patterns for detecting anomalous events has gained more attention in recent years, especially in the video surveillance system. An anomalous event is an event that differs from the normal or usual, but not necessarily in an undesirable manner. The major challenge in detecting such events is the difficulty in creating models due to their unpredictability. Most digital video surveillance systems rely on human observation, which are naturally error prone. Hence, this work validates the rising demand of analysis of video surveillance system. The system being proposed here is of minimum requirements with a competitive computational power when compared to the existing ones.
The main objective of this research work is to build up a framework that recognizes small group of human and to detect the event in the video. A combination of feature extraction using Histogram of Oriented Gradient (HOG) and feature reduction with Principle Component Analysis (PCA) is proposed in this work. The knowledge base and video feed for test cases are classified using the Support Vector Machine (SVM) to categorize the event as either anomalous or not based on various parameters.
The experimental result demonstrates that this approach is able to detect anomalous events with a competitive success rate. The framework can be used to identify various events such as anomalous detection of events, counting people, fall detection, person identification, gender classification, human gait characterization etc.
KeywordsForeground extraction Human group extraction (HOE) Visual saliency Event detection Classification
- 1.Andriluka, M., Roth, S., Schiele, B.: People tracking by detection and people detection by tracking. In: IEEE Computer Vision and Pattern Recognition (2008)Google Scholar
- 3.Li, Y., Huang, C., Nevatia, R.: Learning to associate: HybridBoosted multi-target tracker for crowded scene. In: IEEE Computer Vision and Pattern Recognition (2009)Google Scholar
- 4.Fradkin, D., Muchnik, I.: Support vector machines for classification. DIMACS Series in Discrete Mathematics and Theoretical Computer ScienceGoogle Scholar
- 5.Michel, P., Kaliouby, R.E.: Real time facial expression recognition in video using support vector machines. In: Proceedings of ICMI 2003, pp. 258–264 (2003)Google Scholar
- 6.https://www.quora.com/How-does-one-decide-on-which-kernel-to-choose-for-an-SVM-RBF-vs-linear-vs-poly-kernel answer by Charles H Martin, followed up on https://charlesmartin14.wordpress.com/2012/02/06/kernels_part_1/
- 8.Suriani, N.S., Hussain, A., Zulkifley, M.A.: Sudden event recognition: a survey. Sensors 13(3), 9966–9998 (2008)Google Scholar
- 9.Lin, W., Sun, M.-T., Poovendran, R., Zang, Z.: Group event detection for video surveillance. In: IEEE, pp. 2830–2833 (2009) Google Scholar
- 11.Zaidenberg, S., Boulay, B., Garate, C., Chau, D.P.: Group interaction and group tracking for video-surveillance in underground railway stations. In: International Workshop on Behavior Analysis and Video Understanding (2011)Google Scholar
- 12.Zhang, Y., Ge, W., Chang, M.-C., Liu, X.: Group context learning for event recognition. In: Applications of Computer Vision. IEEE, pp. 249–255 (2012)Google Scholar
- 13.Zhang, D., Chen, F., Tong, C.: Particle motion based abnormal event detection in group-level crowd. J. Converg. Inf. Technol. 7(14) (2012)Google Scholar
- 14.Arbat, S., Sinha, S.K., Shikha, B.K.: Event detection in broadcast soccer video by detecting replays. Int. J. Sci. Technol. Res. 3(5), 282–285 (2014)Google Scholar
- 16.Kumar, A.N., Suresh Kumar, C.: Abnormal crowd detection and tracking in surveillance video sequence. Int. J. Adv. Res. Comput. Commun. Eng. 3(9) (2014)Google Scholar
- 17.Pooka, N.S.: Suspicious group event detection for outdoor environment. Int. J. Mod. Trends Eng. Res. 0X(0Y) (2015)Google Scholar
- 18.Berclaz, J., Fleuret, F., Fua, P.: Robust people tracking with global trajectory optimization. In: IEEE Computer Vision and Pattern Recognition (2006)Google Scholar
- 24.Sivarathinabala, M., Abirami, S.: An intelligent video surveillance framework for remote montioring. Int. J. Eng. Sci. Innov. Technol. IJESIT 2(2), 297–301 (2013)Google Scholar
- 25.Thida, M., Yong, Y.L., Climent-Pérez, P., Eng, H.-l., Remagnino, P.: A literature review on video analytics of crowded scenes. In: Atrey, P.K., Kankanhalli, M.S., Cavallaro, A. (eds.) Intelligent Multimedia Surveillance, pp. 17–36. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41512-8_2CrossRefGoogle Scholar